A Markov decision model for consumer term-loan collections

Zhixin Liu (), Ping He () and Bo Chen ()
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Zhixin Liu: University of Michigan–Dearborn
Ping He: South China University of Technology
Bo Chen: Hefei University of Technology

Review of Quantitative Finance and Accounting, 2019, vol. 52, issue 4, No 5, 1043-1064

Abstract: Abstract We examine how to efficiently schedule collection actions for consumer term-loan accounts over time using a Markov decision model. A consumer loan account at each age can be classified into different account states, including current, delinquent, early payoff, default, and bankrupt. We model state transitions of loan accounts using a Markov transition matrix, and develop an optimization method to determine the collection action at each state and age for each consumer type to maximize the lender’s expected value. The optimization approach incorporates default risk and operational cost, and also addresses the time value of money, the tradeoff between interest revenue and borrowing cost, time consistency in optimization, competing risks between different account states, and penalty for late payment. Compared with a static collection policy, our method is demonstrably more valuable for accounts with high interest rates and medium to high loan amount, especially with stronger collection effects. We also demonstrate how the collection actions implemented under an optimal collection policy are affected by interest rate, loan amount, and collection effects.

Keywords: Dynamic programming; Collection; Markov process; Optimization (search for similar items in EconPapers)
JEL-codes: G21 G23 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11156-018-0735-4

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